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SKU/Artículo: AMZ-B0GCZ4YS9V

Software Testing with Generative AI

Format:

Paperback

Detalles del producto

Disponibilidad:
En stock

Peso con empaque:
0.24 kg

Devolución:
Sí

Condición
Nuevo

Producto de:
Amazon

Viaja desde
USA

Sobre este producto

Software Testing with Generative AIEngineering Reliable Quality Systems in the Age of Probabilistic SoftwareSoftware testing is entering a new era.Generative AI is no longer a futuristic experiment—it is already shaping how test cases are created, how risks are analyzed, how failures are diagnosed, and how quality decisions are made. Yet most existing guidance treats generative AI as a productivity shortcut or a tool to “speed things up,” without addressing the deeper engineering implications.This book takes a different approach.Testing with Generative AI is a rigorous, engineering-focused exploration of how generative models fundamentally change the nature of software testing—and what professionals must do to use them responsibly, safely, and effectively.Rather than focusing on tools, certifications, or surface-level techniques, this book treats generative AI as a probabilistic software system that must be architected, constrained, validated, and governed with the same discipline applied to production systems.What this book coversYou will learn how to:Understand generative AI systems through an engineering lens, including how probabilistic behavior alters traditional testing assumptions.Redefine test oracles, correctness, and validation in environments where outputs are plausible but not guaranteed to be correct.Use prompt engineering as a form of test design, specification, and constraint definition.Apply generative AI across the entire test lifecycle—from requirements analysis to regression maintenance—without eroding trust or accountability.Design architectures for AI-assisted testing that include retrieval grounding, validation layers, and human-in-the-loop controls.Detect, classify, and mitigate hallucinations and other AI-specific failure modes.Establish security, privacy, and trust boundaries when integrating generative AI into quality workflows.Operationalize generative AI in real test organizations, balancing speed, risk, and professional responsibility.Measure success using metrics that reflect confidence, defect detection, and risk reduction—not vanity automation numbers.Throughout the book, concepts are grounded in real-world case studies, engineering reasoning, pseudocode, and system-level thinking, making it suitable for both academic study and industry application.Who this book is forThis book is written for:Software test engineers, SDETs, and QA professionals working with modern systemsSoftware architects and senior developers responsible for quality infrastructureEngineering leaders evaluating how generative AI fits into their delivery pipelinesResearchers and graduate students studying software testing, AI-assisted engineering, or socio-technical systemsWhat this book is notThis is not:A tool manualA certification or exam guideA collection of prompt recipesA claim that AI replaces testersInstead, it is a serious engineering text that argues one central idea:Probabilistic systems demand stronger engineering discipline, not weaker standards.If you are looking to understand how to test with generative AI without surrendering correctness, accountability, or professional judgment this book provides the foundation.

AR$23.956

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AR$23.956

31% OFF

AR$16.522

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